[100% Off] Production-Ready Genai Agent Development With Python
Build, orchestrate, and deploy resilient AI agents in Python — with runnable code (AutoGen, CrewAI, LangGraph)
Requirements
- Solid Python (functions, classes, async basics, pip/venv); this is a developer course
- Comfort calling APIs and reading library docs; no prior agent experience needed
- An LLM API key (OpenAI/Anthropic/Gemini free tiers work) to run the examples; alternatives noted
- A machine with Python 3.10+; every section ships runnable source code to code along
Description
This course contains the use of artificial intelligence.
AI is used to reframe the words, fixing spelling mistakes and grammatical mistakes and audio conversion.
Most AI-agent tutorials stop at a flashy demo that falls apart the moment real traffic, real APIs, and real costs hit it. This course is the developer’s guide to the part that matters: building agents in Python that actually survive production.
It’s built for intermediate-to-advanced Python developers, ML engineers, and architects who have to ship. You’ll first build the agent loop from scratch — perceive, decide, act, with tools and memory — so the frameworks stop being magic. Then you’ll build real agents with AutoGen, CrewAI, and LangGraph, and learn when to reach for each. You’ll integrate tools and external APIs safely, orchestrate multi-agent workflows, and add the things demos skip: resilient error handling, retries, structured-output validation, monitoring, evaluation, and cost control.
Every section ships its full, runnable source code as a downloadable resource, so you code along rather than watch. You’ll build production-grade agents step by step — a tool-using research agent, a multi-agent crew, a stateful LangGraph workflow — each with the guardrails, retries, and observability that make them dependable. The course is honest about the hard parts: where agents are confidently wrong, how token costs explode at scale, and how to keep a human on the high-stakes calls.
Every cost and scaling decision comes with one global, dollar-based example and one India, rupee-based example, so the economics are real wherever you ship. I’ve spent more than twenty years getting tools adopted in real operations — not demoed, adopted. Enrol now and build GenAI agents that hold up in production, with the Python skills to operate them.
Author(s): Ganesh Ravikumar
